Voice response units receive utterances from human callers. These utterances typically correspond to the reason/intent for the human caller's call. These voice response units combined with artificial intelligence systems use a variety of techniques to correctly discern the meaning of the utterance, or the intent of the human caller's utterance.
Conventionally, artificial intelligence systems are powered by one or more group of training datasets. Certain artificial intelligence systems categorize, infer and process live incoming utterances based on the information learned from the one or more training datasets.
The training datasets must be updated continuously in order to keep the artificial intelligence systems up to date. In conventional systems, subject matter experts update the training datasets. Typically, the subject matter experts analyze, populate and curate a set of utterances which are then included in the training datasets.
It would be desirable to review the live utterances received and identify interesting utterances—candidate training utterances, from the live utterances, that can be included in the training dataset. Artificial intelligence systems can monitor live incoming utterances at a rate that generally exceeds the ability of a human subject matter expert to supervise ongoing learning of an artificial intelligence system. Therefore, it would be further desirable to train the artificial intelligence system to identify live utterances that can be used for candidate training utterances.